CAST AI, the leading Kubernetes automation platform, today announced that it was recognized in the Gartner® Hype Cycle™ for Platform Engineering, research that DevOps leaders can use to identify, plan, and implement relevant practices and technologies.
Gartner Hype Cycles are graphic representations of the maturity and adoption of technologies
and applications, and how they are potentially relevant to solving real business problems and
exploiting new opportunities. CAST AI is recognized in the Hype Cycle as a Sample
Vendor for Autonomous Workload Optimization. This emerging category within platform engineering has a high benefit rating and low market penetration (1 to 5%).
The business impact of autonomous workload optimization solutions is significant. As highlighted in the Hype Cycle report: “Automated workload optimization tools enable organizations to optimize their IT costs, and avoid wasting IT resources, while balancing the performance requirements of applications to ensure that customers are not affected. These tools can enable organizations to become more energy efficient and, in the process, take a step closer to their sustainability goals”.
According to Gartner report analysts Manjunath Bhat and Bill Blosen, “Autonomous workload optimization tools maximize performance, while minimizing resource consumption through one or more of three approaches. The first is optimizing the utilization of compute, network, and storage resources. The second is optimizing application performance by fine-tuning the configuration of the underlying runtime platforms. Third, dynamic workload placement executes workloads based on sustainability, reliability, and cost considerations”.
“We believe this recognition underscores CAST AI’s commitment to expanding the capabilities of its Kubernetes automation platform” said Laurent Gil, CAST AI co-founder and CPO.
About CAST AI:
CAST AI is the leading Kubernetes automation platform that cuts AWS, Azure, and GCP customers’ cloud costs by over 50%. CAST AI goes beyond monitoring clusters and making recommendations. The platform utilizes advanced machine learning algorithms to analyze and automatically optimize clusters in real time, reducing customers’ cloud costs, improving performance and security, and boosting DevOps and engineering productivity.
Learn more: https://cast.ai/
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